Overview

Dataset statistics

Number of variables16
Number of observations117
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 KiB
Average record size in memory129.1 B

Variable types

Categorical4
Numeric12

Alerts

partition has constant value "0.0"Constant
model has a high cardinality: 116 distinct valuesHigh cardinality
sales is highly overall correlated with lnsalesHigh correlation
resale is highly overall correlated with price and 5 other fieldsHigh correlation
price is highly overall correlated with resale and 5 other fieldsHigh correlation
engine_s is highly overall correlated with resale and 8 other fieldsHigh correlation
horsepow is highly overall correlated with resale and 7 other fieldsHigh correlation
wheelbas is highly overall correlated with engine_s and 4 other fieldsHigh correlation
width is highly overall correlated with engine_s and 6 other fieldsHigh correlation
length is highly overall correlated with engine_s and 5 other fieldsHigh correlation
curb_wgt is highly overall correlated with resale and 9 other fieldsHigh correlation
fuel_cap is highly overall correlated with resale and 9 other fieldsHigh correlation
mpg is highly overall correlated with resale and 7 other fieldsHigh correlation
lnsales is highly overall correlated with salesHigh correlation
type is highly overall correlated with curb_wgt and 2 other fieldsHigh correlation
model is uniformly distributedUniform
sales has unique valuesUnique

Reproduction

Analysis started2023-03-11 21:36:12.693723
Analysis finished2023-03-11 21:36:31.556500
Duration18.86 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

manufact
Categorical

Distinct26
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Ford
10 
Dodge
Chevrolet
Toyota
Mitsubishi
 
7
Other values (21)
75 

Length

Max length13
Median length9
Mean length6.8290598
Min length3

Characters and Unicode

Total characters799
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st rowAcura
2nd rowAcura
3rd rowAcura
4th rowAudi
5th rowAudi

Common Values

ValueCountFrequency (%)
Ford 10
 
8.5%
Dodge 9
 
7.7%
Chevrolet 8
 
6.8%
Toyota 8
 
6.8%
Mitsubishi 7
 
6.0%
Mercury 6
 
5.1%
Chrysler 5
 
4.3%
Honda 5
 
4.3%
Pontiac 5
 
4.3%
Nissan 5
 
4.3%
Other values (16) 49
41.9%

Length

2023-03-11T21:36:31.660754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ford 10
 
8.5%
dodge 9
 
7.7%
chevrolet 8
 
6.8%
toyota 8
 
6.8%
mitsubishi 7
 
6.0%
mercury 6
 
5.1%
chrysler 5
 
4.3%
honda 5
 
4.3%
pontiac 5
 
4.3%
nissan 5
 
4.3%
Other values (16) 49
41.9%

Most occurring characters

ValueCountFrequency (%)
e 73
 
9.1%
o 70
 
8.8%
r 53
 
6.6%
i 53
 
6.6%
s 48
 
6.0%
a 43
 
5.4%
d 41
 
5.1%
l 37
 
4.6%
n 36
 
4.5%
u 35
 
4.4%
Other values (32) 310
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 670
83.9%
Uppercase Letter 125
 
15.6%
Dash Punctuation 4
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 73
10.9%
o 70
 
10.4%
r 53
 
7.9%
i 53
 
7.9%
s 48
 
7.2%
a 43
 
6.4%
d 41
 
6.1%
l 37
 
5.5%
n 36
 
5.4%
u 35
 
5.2%
Other values (14) 181
27.0%
Uppercase Letter
ValueCountFrequency (%)
M 19
15.2%
C 16
12.8%
P 11
8.8%
B 10
8.0%
F 10
8.0%
D 9
 
7.2%
H 8
 
6.4%
T 8
 
6.4%
A 6
 
4.8%
N 5
 
4.0%
Other values (7) 23
18.4%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 795
99.5%
Common 4
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 73
 
9.2%
o 70
 
8.8%
r 53
 
6.7%
i 53
 
6.7%
s 48
 
6.0%
a 43
 
5.4%
d 41
 
5.2%
l 37
 
4.7%
n 36
 
4.5%
u 35
 
4.4%
Other values (31) 306
38.5%
Common
ValueCountFrequency (%)
- 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 73
 
9.1%
o 70
 
8.8%
r 53
 
6.6%
i 53
 
6.6%
s 48
 
6.0%
a 43
 
5.4%
d 41
 
5.1%
l 37
 
4.6%
n 36
 
4.5%
u 35
 
4.4%
Other values (32) 310
38.8%

model
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct116
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Neon
 
2
Grand Marquis
 
1
Cutlass
 
1
Pathfinder
 
1
Quest
 
1
Other values (111)
111 

Length

Max length17
Median length13
Mean length6.8974359
Min length2

Characters and Unicode

Total characters807
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)98.3%

Sample

1st rowIntegra
2nd rowTL
3rd rowRL
4th rowA4
5th rowA6

Common Values

ValueCountFrequency (%)
Neon 2
 
1.7%
Grand Marquis 1
 
0.9%
Cutlass 1
 
0.9%
Pathfinder 1
 
0.9%
Quest 1
 
0.9%
Maxima 1
 
0.9%
Altima 1
 
0.9%
Sentra 1
 
0.9%
SL-Class 1
 
0.9%
S-Class 1
 
0.9%
Other values (106) 106
90.6%

Length

2023-03-11T21:36:31.776668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
grand 4
 
3.0%
ram 3
 
2.2%
neon 2
 
1.5%
sebring 2
 
1.5%
carrera 2
 
1.5%
coupe 2
 
1.5%
montero 2
 
1.5%
cherokee 2
 
1.5%
a8 1
 
0.7%
328i 1
 
0.7%
Other values (113) 113
84.3%

Most occurring characters

ValueCountFrequency (%)
a 83
 
10.3%
r 76
 
9.4%
e 71
 
8.8%
o 49
 
6.1%
n 48
 
5.9%
i 41
 
5.1%
t 41
 
5.1%
C 35
 
4.3%
l 31
 
3.8%
s 28
 
3.5%
Other values (46) 304
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 601
74.5%
Uppercase Letter 156
 
19.3%
Decimal Number 26
 
3.2%
Space Separator 17
 
2.1%
Dash Punctuation 6
 
0.7%
Other Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 83
13.8%
r 76
12.6%
e 71
11.8%
o 49
8.2%
n 48
8.0%
i 41
 
6.8%
t 41
 
6.8%
l 31
 
5.2%
s 28
 
4.7%
u 24
 
4.0%
Other values (15) 109
18.1%
Uppercase Letter
ValueCountFrequency (%)
C 35
22.4%
S 20
12.8%
A 12
 
7.7%
M 11
 
7.1%
L 9
 
5.8%
R 9
 
5.8%
G 9
 
5.8%
E 8
 
5.1%
V 8
 
5.1%
P 7
 
4.5%
Other values (11) 28
17.9%
Decimal Number
ValueCountFrequency (%)
0 10
38.5%
3 5
19.2%
4 4
 
15.4%
8 3
 
11.5%
2 2
 
7.7%
5 1
 
3.8%
6 1
 
3.8%
Space Separator
ValueCountFrequency (%)
17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 757
93.8%
Common 50
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 83
 
11.0%
r 76
 
10.0%
e 71
 
9.4%
o 49
 
6.5%
n 48
 
6.3%
i 41
 
5.4%
t 41
 
5.4%
C 35
 
4.6%
l 31
 
4.1%
s 28
 
3.7%
Other values (36) 254
33.6%
Common
ValueCountFrequency (%)
17
34.0%
0 10
20.0%
- 6
 
12.0%
3 5
 
10.0%
4 4
 
8.0%
8 3
 
6.0%
2 2
 
4.0%
. 1
 
2.0%
5 1
 
2.0%
6 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 83
 
10.3%
r 76
 
9.4%
e 71
 
8.8%
o 49
 
6.1%
n 48
 
5.9%
i 41
 
5.1%
t 41
 
5.1%
C 35
 
4.3%
l 31
 
3.8%
s 28
 
3.5%
Other values (46) 304
37.7%

sales
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct117
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.112316
Minimum0.11
Maximum540.561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:31.908298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile3.022
Q116.767
median32.299
Q376.029
95-th percentile221.9322
Maximum540.561
Range540.451
Interquartile range (IQR)59.262

Descriptive statistics

Standard deviation75.058933
Coefficient of variation (CV)1.2697681
Kurtosis14.676451
Mean59.112316
Median Absolute Deviation (MAD)23.068
Skewness3.1699938
Sum6916.141
Variance5633.8435
MonotonicityNot monotonic
2023-03-11T21:36:32.075157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.919 1
 
0.9%
81.174 1
 
0.9%
1.112 1
 
0.9%
42.574 1
 
0.9%
27.308 1
 
0.9%
79.853 1
 
0.9%
88.094 1
 
0.9%
42.643 1
 
0.9%
3.311 1
 
0.9%
16.774 1
 
0.9%
Other values (107) 107
91.5%
ValueCountFrequency (%)
0.11 1
0.9%
0.916 1
0.9%
1.112 1
0.9%
1.28 1
0.9%
1.38 1
0.9%
1.866 1
0.9%
3.311 1
0.9%
4.734 1
0.9%
5.223 1
0.9%
5.24 1
0.9%
ValueCountFrequency (%)
540.561 1
0.9%
276.747 1
0.9%
247.994 1
0.9%
245.815 1
0.9%
230.902 1
0.9%
227.061 1
0.9%
220.65 1
0.9%
199.685 1
0.9%
181.749 1
0.9%
157.04 1
0.9%

resale
Real number (ℝ)

Distinct114
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.031538
Minimum5.16
Maximum67.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:32.223158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5.16
5-th percentile7.845
Q111.24
median14.01
Q319.875
95-th percentile41.29
Maximum67.55
Range62.39
Interquartile range (IQR)8.635

Descriptive statistics

Standard deviation11.605632
Coefficient of variation (CV)0.64362962
Kurtosis5.64035
Mean18.031538
Median Absolute Deviation (MAD)3.8
Skewness2.2909972
Sum2109.69
Variance134.6907
MonotonicityNot monotonic
2023-03-11T21:36:32.369472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.75 2
 
1.7%
12.025 2
 
1.7%
16.575 2
 
1.7%
11.03 1
 
0.9%
15.38 1
 
0.9%
15.125 1
 
0.9%
11.295 1
 
0.9%
8.45 1
 
0.9%
58.6 1
 
0.9%
50.375 1
 
0.9%
Other values (104) 104
88.9%
ValueCountFrequency (%)
5.16 1
0.9%
5.86 1
0.9%
7.425 1
0.9%
7.75 2
1.7%
7.825 1
0.9%
7.85 1
0.9%
8.325 1
0.9%
8.45 1
0.9%
8.8 1
0.9%
8.835 1
0.9%
ValueCountFrequency (%)
67.55 1
0.9%
60.625 1
0.9%
58.6 1
0.9%
58.47 1
0.9%
50.375 1
0.9%
41.45 1
0.9%
41.25 1
0.9%
40.375 1
0.9%
39 1
0.9%
36.225 1
0.9%

type
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0.0
88 
1.0
29 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters351
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 88
75.2%
1.0 29
 
24.8%

Length

2023-03-11T21:36:32.501896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-11T21:36:32.623057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 88
75.2%
1.0 29
 
24.8%

Most occurring characters

ValueCountFrequency (%)
0 205
58.4%
. 117
33.3%
1 29
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 234
66.7%
Other Punctuation 117
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 205
87.6%
1 29
 
12.4%
Other Punctuation
ValueCountFrequency (%)
. 117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 205
58.4%
. 117
33.3%
1 29
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 205
58.4%
. 117
33.3%
1 29
 
8.3%

price
Real number (ℝ)

Distinct116
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.969487
Minimum9.235
Maximum82.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:32.741484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.235
5-th percentile12.066
Q116.98
median21.665
Q329.465
95-th percentile55.604
Maximum82.6
Range73.365
Interquartile range (IQR)12.485

Descriptive statistics

Standard deviation14.149699
Coefficient of variation (CV)0.54485864
Kurtosis4.152498
Mean25.969487
Median Absolute Deviation (MAD)5.27
Skewness1.9449098
Sum3038.43
Variance200.21399
MonotonicityNot monotonic
2023-03-11T21:36:32.885068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.64 2
 
1.7%
22.605 1
 
0.9%
18.145 1
 
0.9%
29.299 1
 
0.9%
26.399 1
 
0.9%
26.249 1
 
0.9%
20.39 1
 
0.9%
13.499 1
 
0.9%
82.6 1
 
0.9%
69.7 1
 
0.9%
Other values (106) 106
90.6%
ValueCountFrequency (%)
9.235 1
0.9%
9.699 1
0.9%
10.685 1
0.9%
11.528 1
0.9%
11.799 1
0.9%
12.05 1
0.9%
12.07 1
0.9%
12.535 1
0.9%
12.64 2
1.7%
12.885 1
0.9%
ValueCountFrequency (%)
82.6 1
0.9%
74.97 1
0.9%
71.02 1
0.9%
69.725 1
0.9%
69.7 1
0.9%
62 1
0.9%
54.005 1
0.9%
51.728 1
0.9%
49.9 1
0.9%
45.705 1
0.9%

engine_s
Real number (ℝ)

Distinct29
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0487179
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:33.018530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.8
Q12.2
median3
Q33.8
95-th percentile4.6
Maximum8
Range7
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0551692
Coefficient of variation (CV)0.34610259
Kurtosis2.9905609
Mean3.0487179
Median Absolute Deviation (MAD)0.8
Skewness1.1226249
Sum356.7
Variance1.113382
MonotonicityNot monotonic
2023-03-11T21:36:33.137204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 14
 
12.0%
3 11
 
9.4%
1.8 8
 
6.8%
3.8 8
 
6.8%
4.6 8
 
6.8%
2.4 8
 
6.8%
2.5 8
 
6.8%
4 6
 
5.1%
3.5 5
 
4.3%
3.4 5
 
4.3%
Other values (19) 36
30.8%
ValueCountFrequency (%)
1 1
 
0.9%
1.5 1
 
0.9%
1.6 1
 
0.9%
1.8 8
6.8%
1.9 3
 
2.6%
2 14
12.0%
2.2 2
 
1.7%
2.3 2
 
1.7%
2.4 8
6.8%
2.5 8
6.8%
ValueCountFrequency (%)
8 1
 
0.9%
5.7 1
 
0.9%
5.2 1
 
0.9%
5 1
 
0.9%
4.7 1
 
0.9%
4.6 8
6.8%
4.3 2
 
1.7%
4.2 1
 
0.9%
4 6
5.1%
3.9 2
 
1.7%

horsepow
Real number (ℝ)

Distinct57
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.28205
Minimum55
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:33.278531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile112.4
Q1140
median175
Q3210
95-th percentile292
Maximum450
Range395
Interquartile range (IQR)70

Descriptive statistics

Standard deviation58.591786
Coefficient of variation (CV)0.32320787
Kurtosis3.1905199
Mean181.28205
Median Absolute Deviation (MAD)35
Skewness1.2168365
Sum21210
Variance3432.9973
MonotonicityNot monotonic
2023-03-11T21:36:33.440181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 8
 
6.8%
200 8
 
6.8%
210 7
 
6.0%
115 5
 
4.3%
175 5
 
4.3%
170 5
 
4.3%
132 4
 
3.4%
120 4
 
3.4%
205 4
 
3.4%
275 4
 
3.4%
Other values (47) 63
53.8%
ValueCountFrequency (%)
55 1
 
0.9%
92 1
 
0.9%
100 2
 
1.7%
106 1
 
0.9%
110 1
 
0.9%
113 1
 
0.9%
115 5
4.3%
119 1
 
0.9%
120 4
3.4%
124 1
 
0.9%
ValueCountFrequency (%)
450 1
 
0.9%
345 1
 
0.9%
310 1
 
0.9%
302 1
 
0.9%
300 2
1.7%
290 1
 
0.9%
275 4
3.4%
253 1
 
0.9%
250 1
 
0.9%
240 2
1.7%

wheelbas
Real number (ℝ)

Distinct74
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.3265
Minimum92.6
Maximum138.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:33.594542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum92.6
5-th percentile95.92
Q1102.4
median107
Q3111.6
95-th percentile120.14
Maximum138.7
Range46.1
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation8.0505878
Coefficient of variation (CV)0.075010255
Kurtosis3.2021187
Mean107.3265
Median Absolute Deviation (MAD)4.6
Skewness1.1617449
Sum12557.2
Variance64.811964
MonotonicityNot monotonic
2023-03-11T21:36:33.740171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.2 6
 
5.1%
102.4 4
 
3.4%
107 4
 
3.4%
108 4
 
3.4%
106.4 3
 
2.6%
109 3
 
2.6%
103.7 3
 
2.6%
107.3 3
 
2.6%
113 3
 
2.6%
98.9 3
 
2.6%
Other values (64) 81
69.2%
ValueCountFrequency (%)
92.6 2
1.7%
93.1 1
0.9%
93.4 1
0.9%
94.9 1
0.9%
95.2 1
0.9%
96.1 1
0.9%
96.2 1
0.9%
97 1
0.9%
97.1 1
0.9%
97.2 1
0.9%
ValueCountFrequency (%)
138.7 1
0.9%
138.5 1
0.9%
131 1
0.9%
127.2 1
0.9%
121.5 1
0.9%
120.7 1
0.9%
120 1
0.9%
119 1
0.9%
118.1 1
0.9%
117.7 1
0.9%

width
Real number (ℝ)

Distinct66
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.189744
Minimum62.6
Maximum79.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:33.893367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum62.6
5-th percentile66.5
Q168.5
median70.4
Q373.6
95-th percentile78.2
Maximum79.3
Range16.7
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation3.5301505
Coefficient of variation (CV)0.049587909
Kurtosis-0.34978062
Mean71.189744
Median Absolute Deviation (MAD)2.3
Skewness0.42964929
Sum8329.2
Variance12.461963
MonotonicityNot monotonic
2023-03-11T21:36:34.041382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.7 6
 
5.1%
74.4 5
 
4.3%
70.3 5
 
4.3%
68.3 4
 
3.4%
69.1 4
 
3.4%
69.4 4
 
3.4%
72.7 4
 
3.4%
66.5 3
 
2.6%
70.2 3
 
2.6%
71 3
 
2.6%
Other values (56) 76
65.0%
ValueCountFrequency (%)
62.6 1
 
0.9%
65.7 1
 
0.9%
66.4 3
2.6%
66.5 3
2.6%
66.7 6
5.1%
66.9 1
 
0.9%
67 1
 
0.9%
67.1 1
 
0.9%
67.3 2
 
1.7%
67.7 1
 
0.9%
ValueCountFrequency (%)
79.3 1
 
0.9%
79.1 1
 
0.9%
78.8 2
1.7%
78.7 1
 
0.9%
78.2 3
2.6%
76.8 2
1.7%
76.6 1
 
0.9%
76.4 1
 
0.9%
76.1 1
 
0.9%
75.7 1
 
0.9%

length
Real number (ℝ)

Distinct102
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.71795
Minimum149.4
Maximum224.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:34.191292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum149.4
5-th percentile166.12
Q1177.5
median187.8
Q3196.5
95-th percentile209.68
Maximum224.5
Range75.1
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.849926
Coefficient of variation (CV)0.07378051
Kurtosis0.26477714
Mean187.71795
Median Absolute Deviation (MAD)9.8
Skewness0.067233008
Sum21963
Variance191.82045
MonotonicityNot monotonic
2023-03-11T21:36:34.332520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186.3 4
 
3.4%
174 2
 
1.7%
194.8 2
 
1.7%
190.2 2
 
1.7%
174.4 2
 
1.7%
186 2
 
1.7%
212 2
 
1.7%
200.9 2
 
1.7%
176.9 2
 
1.7%
174.5 2
 
1.7%
Other values (92) 95
81.2%
ValueCountFrequency (%)
149.4 1
0.9%
152 1
0.9%
160.4 1
0.9%
163.3 2
1.7%
163.8 1
0.9%
166.7 1
0.9%
167.5 1
0.9%
170.5 1
0.9%
171 1
0.9%
172.3 1
0.9%
ValueCountFrequency (%)
224.5 1
0.9%
224.2 1
0.9%
215.3 1
0.9%
215 1
0.9%
212 2
1.7%
209.1 1
0.9%
208.5 2
1.7%
207.7 1
0.9%
207.2 1
0.9%
206.8 1
0.9%

curb_wgt
Real number (ℝ)

Distinct113
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3240513
Minimum1.895
Maximum5.115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:34.476672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.895
5-th percentile2.3918
Q12.911
median3.34
Q33.823
95-th percentile4.2536
Maximum5.115
Range3.22
Interquartile range (IQR)0.912

Descriptive statistics

Standard deviation0.59717673
Coefficient of variation (CV)0.17965328
Kurtosis-0.10254827
Mean3.3240513
Median Absolute Deviation (MAD)0.434
Skewness0.20850444
Sum388.914
Variance0.35662005
MonotonicityNot monotonic
2023-03-11T21:36:34.608011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.998 2
 
1.7%
3.368 2
 
1.7%
2.769 2
 
1.7%
3.876 2
 
1.7%
2.639 1
 
0.9%
3.944 1
 
0.9%
3.102 1
 
0.9%
3.947 1
 
0.9%
3.991 1
 
0.9%
3.294 1
 
0.9%
Other values (103) 103
88.0%
ValueCountFrequency (%)
1.895 1
0.9%
2.24 1
0.9%
2.25 1
0.9%
2.332 1
0.9%
2.339 1
0.9%
2.367 1
0.9%
2.398 1
0.9%
2.42 1
0.9%
2.425 1
0.9%
2.452 1
0.9%
ValueCountFrequency (%)
5.115 1
0.9%
4.808 1
0.9%
4.52 1
0.9%
4.47 1
0.9%
4.298 1
0.9%
4.288 1
0.9%
4.245 1
0.9%
4.241 1
0.9%
4.133 1
0.9%
4.125 1
0.9%

fuel_cap
Real number (ℝ)

Distinct45
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.812821
Minimum10.3
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:34.777224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10.3
5-th percentile12.42
Q115.3
median17.2
Q319.8
95-th percentile25.16
Maximum32
Range21.7
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.7946091
Coefficient of variation (CV)0.21302685
Kurtosis2.3463759
Mean17.812821
Median Absolute Deviation (MAD)2
Skewness1.0942601
Sum2084.1
Variance14.399058
MonotonicityNot monotonic
2023-03-11T21:36:34.913167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
18.5 11
 
9.4%
20 8
 
6.8%
16 7
 
6.0%
19 7
 
6.0%
17 6
 
5.1%
17.5 5
 
4.3%
13.2 5
 
4.3%
14.5 4
 
3.4%
15 4
 
3.4%
15.9 4
 
3.4%
Other values (35) 56
47.9%
ValueCountFrequency (%)
10.3 1
 
0.9%
11.9 2
 
1.7%
12.1 3
2.6%
12.5 2
 
1.7%
12.7 1
 
0.9%
13.2 5
4.3%
13.7 1
 
0.9%
14.3 1
 
0.9%
14.5 4
3.4%
14.6 1
 
0.9%
ValueCountFrequency (%)
32 2
1.7%
26 3
2.6%
25.4 1
 
0.9%
25.1 1
 
0.9%
25 1
 
0.9%
24.3 1
 
0.9%
23.7 1
 
0.9%
23.2 1
 
0.9%
22.5 1
 
0.9%
22 1
 
0.9%

mpg
Real number (ℝ)

Distinct21
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.117949
Minimum15
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-03-11T21:36:35.043006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile17
Q122
median24
Q326
95-th percentile31.2
Maximum45
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.4041637
Coefficient of variation (CV)0.18260938
Kurtosis3.5622496
Mean24.117949
Median Absolute Deviation (MAD)2
Skewness0.91264208
Sum2821.8
Variance19.396658
MonotonicityNot monotonic
2023-03-11T21:36:35.146507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
25 17
14.5%
24 14
12.0%
22 12
10.3%
27 11
9.4%
21 10
8.5%
23 9
 
7.7%
26 7
 
6.0%
19 6
 
5.1%
33 4
 
3.4%
30 4
 
3.4%
Other values (11) 23
19.7%
ValueCountFrequency (%)
15 2
 
1.7%
16 3
 
2.6%
17 2
 
1.7%
18 3
 
2.6%
19 6
5.1%
20 3
 
2.6%
21 10
8.5%
22 12
10.3%
23 9
7.7%
24 14
12.0%
ValueCountFrequency (%)
45 1
 
0.9%
33 4
 
3.4%
32 1
 
0.9%
31 3
 
2.6%
30 4
 
3.4%
29 2
 
1.7%
28 2
 
1.7%
27 11
9.4%
26 7
6.0%
25 17
14.5%

lnsales
Real number (ℝ)

Distinct112
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4011795
Minimum-2.207
Maximum6.293
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)1.7%
Memory size1.0 KiB
2023-03-11T21:36:35.277798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.207
5-th percentile1.0824
Q12.819
median3.475
Q34.331
95-th percentile5.4026
Maximum6.293
Range8.5
Interquartile range (IQR)1.512

Descriptive statistics

Standard deviation1.3378623
Coefficient of variation (CV)0.39335247
Kurtosis2.236314
Mean3.4011795
Median Absolute Deviation (MAD)0.811
Skewness-0.94595299
Sum397.938
Variance1.7898756
MonotonicityNot monotonic
2023-03-11T21:36:35.419811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.318 2
 
1.7%
3.015 2
 
1.7%
4.331 2
 
1.7%
3.475 2
 
1.7%
3.672 2
 
1.7%
4.38 1
 
0.9%
2.82 1
 
0.9%
1.197 1
 
0.9%
3.753 1
 
0.9%
4.478 1
 
0.9%
Other values (102) 102
87.2%
ValueCountFrequency (%)
-2.207 1
0.9%
-0.088 1
0.9%
0.106 1
0.9%
0.247 1
0.9%
0.322 1
0.9%
0.624 1
0.9%
1.197 1
0.9%
1.555 1
0.9%
1.653 1
0.9%
1.656 1
0.9%
ValueCountFrequency (%)
6.293 1
0.9%
5.623 1
0.9%
5.513 1
0.9%
5.505 1
0.9%
5.442 1
0.9%
5.425 1
0.9%
5.397 1
0.9%
5.297 1
0.9%
5.203 1
0.9%
5.057 1
0.9%

partition
Categorical

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0.0
117 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters351
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 117
100.0%

Length

2023-03-11T21:36:36.088744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-11T21:36:36.197275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 117
100.0%

Most occurring characters

ValueCountFrequency (%)
0 234
66.7%
. 117
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 234
66.7%
Other Punctuation 117
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234
100.0%
Other Punctuation
ValueCountFrequency (%)
. 117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 234
66.7%
. 117
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 234
66.7%
. 117
33.3%

Interactions

2023-03-11T21:36:29.917008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:13.436029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:14.778257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:16.172254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:17.592569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:19.033152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:21.375226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:22.731565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:24.186712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:25.580855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:26.913840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:28.634997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.009864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:13.535443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:14.905450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:16.281894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:17.716733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:19.149864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:21.473999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:22.856308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:24.295883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:25.685292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:27.030899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:28.744930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.126015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:13.643965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:15.020525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:16.400287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:17.828953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:19.277865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:21.576499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:22.968852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:24.409349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:25.796901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:27.149092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:28.849387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.225335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:13.755478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:15.155391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:16.530449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:17.947845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:19.404931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:21.687169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:23.118173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:24.536418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:25.908406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:27.251067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:28.973757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.329684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:13.865579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:15.270699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:16.647055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:18.072010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:19.515349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:21.805074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:23.251549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:24.675753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:26.016387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:27.365911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:29.087822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.432843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:13.975416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:15.395324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:16.775883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:18.196983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:19.635580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:21.916121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:23.392841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:24.795641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:26.134292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:27.484138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:29.204371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.545412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:14.094447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:15.507292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:16.894181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:18.313011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:19.749710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:22.028105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:23.504732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:24.906541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:26.239411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:27.590712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:29.305767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.651465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:14.212573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:15.618711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:17.013520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:18.445821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:19.873440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:22.145115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:23.626314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:25.016484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:26.368833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:27.707469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:29.414524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.767262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:14.333867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:15.733198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:17.132237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:18.570105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:19.995248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:22.250165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:23.736065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:25.137457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:26.476929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:27.807263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:29.526166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.868886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:14.445599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:15.848951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:17.257836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:18.689532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:21.036097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:22.370275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:23.849903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:25.254493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:26.593374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:28.332211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:29.628391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:30.959220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:14.565134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:15.949614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:17.370388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:18.792058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:21.152174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:22.472938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:23.957923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:25.365861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:26.695399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:28.447941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:29.724529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:31.066422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:14.678225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:16.071477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:17.487554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:18.916585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:21.267838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:22.591742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:24.074176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:25.473577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:26.808836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:28.539813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-11T21:36:29.825041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-11T21:36:36.281569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
salesresalepriceengine_shorsepowwheelbaswidthlengthcurb_wgtfuel_capmpglnsalesmanufacttype
sales1.000-0.440-0.392-0.097-0.2670.1780.0280.124-0.066-0.0480.1251.0000.0000.205
resale-0.4401.0000.9190.5950.7710.2440.3390.2350.6240.610-0.579-0.4400.2860.237
price-0.3920.9191.0000.7400.8980.3690.4800.3930.7270.653-0.642-0.3910.3410.144
engine_s-0.0970.5950.7401.0000.8580.5540.7100.6390.8480.751-0.796-0.0960.2400.237
horsepow-0.2670.7710.8980.8581.0000.4640.6100.5410.7390.660-0.647-0.2670.2070.000
wheelbas0.1780.2440.3690.5540.4641.0000.7010.8740.7530.661-0.4700.1780.0000.396
width0.0280.3390.4800.7100.6100.7011.0000.7340.7370.654-0.5490.0280.1580.328
length0.1240.2350.3930.6390.5410.8740.7341.0000.7270.588-0.4470.1240.1200.069
curb_wgt-0.0660.6240.7270.8480.7390.7530.7370.7271.0000.877-0.809-0.0660.0720.503
fuel_cap-0.0480.6100.6530.7510.6600.6610.6540.5880.8771.000-0.843-0.0470.2690.602
mpg0.125-0.579-0.642-0.796-0.647-0.470-0.549-0.447-0.809-0.8431.0000.1250.2370.609
lnsales1.000-0.440-0.391-0.096-0.2670.1780.0280.124-0.066-0.0470.1251.0000.1350.160
manufact0.0000.2860.3410.2400.2070.0000.1580.1200.0720.2690.2370.1351.0000.406
type0.2050.2370.1440.2370.0000.3960.3280.0690.5030.6020.6090.1600.4061.000

Missing values

2023-03-11T21:36:31.232066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-11T21:36:31.458930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

manufactmodelsalesresaletypepriceengine_shorsepowwheelbaswidthlengthcurb_wgtfuel_capmpglnsalespartition
0AcuraIntegra16.91916.3600.021.5001.8140.0101.267.3172.42.63913.228.02.8280.0
1AcuraTL39.38419.8750.028.4003.2225.0108.170.3192.93.51717.225.03.6730.0
2AcuraRL8.58829.7250.042.0003.5210.0114.671.4196.63.85018.022.02.1500.0
3AudiA420.39722.2550.023.9901.8150.0102.668.2178.02.99816.427.03.0150.0
4AudiA618.78023.5550.033.9502.8200.0108.776.1192.03.56118.522.02.9330.0
5AudiA81.38039.0000.062.0004.2310.0113.074.0198.23.90223.721.00.3220.0
6BMW328i9.23128.6750.033.4002.8193.0107.368.5176.03.19716.624.02.2230.0
7BMW528i17.52736.1250.038.9002.8193.0111.470.9188.03.47218.524.82.8640.0
8BuickCentury91.56112.4750.021.9753.1175.0109.072.7194.63.36817.525.04.5170.0
9BuickRegal39.35013.7400.025.3003.8240.0109.072.7196.23.54317.523.03.6720.0
manufactmodelsalesresaletypepriceengine_shorsepowwheelbaswidthlengthcurb_wgtfuel_capmpglnsalespartition
107ToyotaCelica33.26915.4450.016.8751.8140.0102.468.3170.52.42514.531.03.5050.0
108ToyotaTacoma84.0879.5751.011.5282.4142.0103.366.5178.72.58015.123.04.4320.0
109ToyotaRAV425.10613.3251.016.8882.0127.094.966.7163.82.66815.327.03.2230.0
110Toyota4Runner68.41119.4251.022.2882.7150.0105.366.5183.33.44018.523.04.2260.0
111ToyotaLand Cruiser9.83534.0801.051.7284.7230.0112.276.4192.55.11525.415.02.2860.0
112VolkswagenGolf9.76111.4250.014.9002.0115.098.968.3163.32.76714.526.02.2780.0
113VolkswagenJetta83.72113.2400.016.7002.0115.098.968.3172.32.85314.526.04.4270.0
114VolkswagenPassat51.10216.7250.021.2001.8150.0106.468.5184.13.04316.427.03.9340.0
115VolkswagenCabrio9.56916.5750.019.9902.0115.097.466.7160.43.07913.726.02.2590.0
116VolkswagenGTI5.59613.7600.017.5002.0115.098.968.3163.32.76214.626.01.7220.0